TIIS 2013-04 Volume 3 Issue 1

This editorial introduction first outlines some of the research challenges
raised by the emerging forms of internet-scale human problem solving. It then
explains how the two articles in this special section can serve as illuminating
complementary case studies, providing concrete examples embedded in general
conceptual frameworks.

A method of organizing the crowd to generate ideas is described. It
integrates crowds using evolutionary algorithms. The method increases the
creativity of ideas across generations, and it works better than greenfield
idea generation. Specifically, a design space of internet-scale idea generation
systems is defined, and one instance is tested: a crowd idea generation system
that uses combination to improve previous designs. The key process of the
system is the following: A crowd generates designs, then another crowd combines
the designs of the previous crowd. In an experiment with 540 participants, the
combined designs were compared to the initial designs and to the designs
produced by a greenfield idea generation system. The results show that the
sequential combination system produced more creative ideas in the last
generation and outperformed the greenfield idea generation system. The design
space of crowdsourced idea generation developed here may be used to instantiate
systems that can be applied to a wide range of design problems. The work has
both pragmatic and theoretical implications: New forms of coordination are now
possible, and, using the crowd, it is possible to test existing and emerging
theories of coordination and participatory design. Moreover, it may be possible
for human designers, organized as a crowd, to codesign with each other and with
automated algorithms.

We are witnessing a paradigm shift in Human Language Technology (HLT) that
may well have an impact on the field comparable to the statistical revolution:
acquiring large-scale resources by exploiting collective intelligence. An
illustration of this new approach is Phrase Detectives, an interactive online
game with a purpose for creating anaphorically annotated resources that makes
use of a highly distributed population of contributors with different levels of
expertise.
The purpose of this article is to first of all give an overview of all
aspects of Phrase Detectives, from the design of the game and the HLT methods
we used to the results we have obtained so far. It furthermore summarizes the
lessons that we have learned in developing this game which should help other
researchers to design and implement similar games.

Interacting with social networks of intelligent things and people in the
world of gastronomy

This article introduces a framework for creating rich augmented environments
based on a social web of intelligent things and people. We target outdoor
environments, aiming to transform a region into a smart environment that can
share its cultural heritage with people, promoting itself and its special
qualities. Using the applications developed in the framework, people can
interact with things, listen to the stories that these things tell them, and
make their own contributions. The things are intelligent in the sense that they
aggregate information provided by users and behave in a socially active way.
They can autonomously establish social relationships on the basis of their
properties and their interaction with users. Hence when a user gets in touch
with a thing, she is also introduced to its social network consisting of other
things and of users; she can navigate this network to discover and explore the
world around the thing itself. Thus the system supports serendipitous
navigation in a network of things and people that evolves according to the
behavior of users. An innovative interaction model was defined that allows
users to interact with objects in a natural, playful way using smartphones
without the need for a specially created infrastructure.
The framework was instantiated into a suite of applications called WantEat,
in which objects from the domain of tourism and gastronomy (such as cheese
wheels or bottles of wine) are taken as testimonials of the cultural roots of a
region. WantEat includes an application that allows the definition and
registration of things, a mobile application that allows users to interact with
things, and an application that supports stakeholders in getting feedback about
the things that they have registered in the system. WantEat was developed and
tested in a real-world context which involved a region and gastronomy-related
items from it (such as products, shops, restaurants, and recipes), through an
early evaluation with stakeholders and a final evaluation with hundreds of
users.

picoTrans is a prototype system that introduces a novel icon-based paradigm
for cross-lingual communication on mobile devices. Our approach marries a
machine translation system with the popular picture book. Users interact with
picoTrans by pointing at pictures as if it were a picture book; the system
generates natural language from these icons and the user is able to interact
with the icon sequence to refine the meaning of the words that are generated.
When users are satisfied that the sentence generated represents what they wish
to express, they tap a translate button and picoTrans displays the translation.
Structuring the process of communication in this way has many advantages.
First, tapping icons is a very natural method of user input on mobile devices;
typing is cumbersome and speech input errorful. Second, the sequence of icons
which is annotated both with pictures and bilingually with words is meaningful
to both users, and it opens up a second channel of communication between them
that conveys the gist of what is being expressed. We performed a number of
evaluations of picoTrans to determine: its coverage of a corpus of in-domain
sentences; the input efficiency in terms of the number of key presses required
relative to text entry; and users' overall impressions of using the system
compared to using a picture book. Our results show that we are able to cover
74% of the expressions in our test corpus using a 2000-icon set; we believe
that this icon set size is realistic for a mobile device. We also found that
picoTrans requires fewer key presses than typing the input and that the system
is able to predict the correct, intended natural language sentence from the
icon sequence most of the time, making user interaction with the icon sequence
often unnecessary. In the user evaluation, we found that in general users
prefer using picoTrans and are able to communicate more rapidly and
expressively. Furthermore, users had more confidence that they were able to
communicate effectively using picoTrans.

TIIS 2013-07 Volume 3 Issue 2

Smart objects can be smart because of the information and communication
technology that is added to human-made artifacts. It is not, however, the
technology itself that makes them smart but rather the way in which the
technology is integrated, and their smartness surfaces through how people are
able to interact with these objects. Hence, the key challenge for making smart
objects successful is to design usable and useful interactions with them. We
list five features that can contribute to the smartness of an object, and we
discuss how smart objects can help resolve the simplicity-featurism paradox. We
conclude by introducing the three articles in this special issue, which dive
into various aspects of smart object interaction: augmenting objects with
projection, service-oriented interaction with smart objects via a mobile
portal, and an analysis of input-output relations in interaction with tangible
smart objects.

Sensors, processors, and radios can be integrated invisibly into objects to
make them smart and sensitive to user interaction, but feedback is often
limited to beeps, blinks, or buzzes. We propose to redress this input-output
imbalance by augmentation of smart objects with projected displays, that --
unlike physical displays -- allow seamless integration with the natural
appearance of an object. In this article, we investigate how, in a ubiquitous
computing world, smart objects can acquire and control a projection. We
consider that projectors and cameras are ubiquitous in the environment, and we
develop a novel conception and system that enables smart objects to
spontaneously associate with projector-camera systems for cooperative
augmentation. Projector-camera systems are conceived as generic, supporting
standard computer vision methods for different appearance cues, and smart
objects provide a model of their appearance for method selection at runtime, as
well as sensor observations to constrain the visual detection process.
Cooperative detection results in accurate location and pose of the object,
which is then tracked for visual augmentation in response to display requests
by the smart object. In this article, we define the conceptual framework
underlying our approach; report on computer vision experiments that give
original insight into natural appearance-based detection of everyday objects;
show how object sensing can be used to increase speed and robustness of visual
detection; describe and evaluate a fully implemented system; and describe two
smart object applications to illustrate the system's cooperative augmentation
process and the embodied interactions it enables with smart objects.

Embodying services into physical places: Toward the design of a mobile
environment browser

The tremendous developments in mobile computing and handheld devices have
allowed for an increasing usage of the resources of the World Wide Web. People
today consume information and services on the go, through smart phones
applications capable of exploiting their location in order to adapt the content
according to the context of use. As location-based services gain traction and
reveal their limitations, we argue there is a need for intelligent systems to
be created to better support people's activities in their experience of the
city, especially regarding their decision-making processes. In this article, we
explore the opportunity to move closer to the realization of the ubiquitous
computing vision by turning physical places into smart environments capable of
cooperatively and autonomously collecting, processing, and transporting
information about their characteristics (e.g., practical information, presence
of people, and ambience). Following a multidisciplinary approach which
leverages psychology, design, and computer science, we propose to investigate
the potential of building communication and interaction spaces, called
information spheres, on top of physical places such as businesses, homes, and
institutions. We argue that, if the latter are exposed on the Web, they can act
as a platform delivering information and services and mediating interactions
with smart objects without requiring too much effort for the deployment of the
architecture. After presenting the inherent challenges of our vision, we go
through the protocol of two preliminary experiments that aim to evaluate users'
perception of different types of information (i.e., reviews, check-in
information, video streams, and real-time representations) and their influence
on the decision-making process. Results of this study lead us to elaborate the
design considerations that must be taken into account to ensure the
intelligibility and user acceptance of information spheres. We finally describe
a research prototype application called Environment Browser (Env-B) and present
the underlying smart space middleware, before evaluating the user experience
with our system through quantitative and qualitative methods.

An analysis of input-output relations in interaction with smart tangible
objects

This article focuses on the conceptual relation between the user's input and
a system's output in interaction with smart tangible objects. Understanding
this input-output relation (IO relation) is a prerequisite for the design of
meaningful interaction. A meaningful IO relation allows the user to know what
to do with a system to achieve a certain goal and to evaluate the outcome. The
work discussed in this article followed a design research process in which four
concepts were developed and prototyped. An evaluation was performed using these
prototypes to investigate the effect of highly different IO relations on the
user's understanding of the interaction. The evaluation revealed two types of
IO relations differing in functionality and the number of mappings between the
user and system actions. These two types of relations are described by two IO
models that provide an overview of these mappings. Furthermore, they illustrate
the role of the user and the influence of the system in the process of
understanding the interaction. The analysis of the two types of IO models
illustrates the value of understanding IO relations for the design of smart
tangible objects.

Special section on eye gaze and conversation

This editorial introduction first explains the origin of this special
section. It then outlines how each of the two articles included sheds light on
possibilities for conversational dialog systems to use eye gaze as a signal
that reflects aspects of participation in the dialog: degree of engagement and
turn taking behavior, respectively.

In face-to-face conversations, speakers are continuously checking whether
the listener is engaged in the conversation, and they change their
conversational strategy if the listener is not fully engaged. With the goal of
building a conversational agent that can adaptively control conversations, in
this study we analyze listener gaze behaviors and develop a method for
estimating whether a listener is engaged in the conversation on the basis of
these behaviors. First, we conduct a Wizard-of-Oz study to collect information
on a user's gaze behaviors. We then investigate how conversational
disengagement, as annotated by human judges, correlates with gaze transition,
mutual gaze (eye contact) occurrence, gaze duration, and eye movement distance.
On the basis of the results of these analyses, we identify useful information
for estimating a user's disengagement and establish an engagement estimation
method using a decision tree technique. The results of these analyses show that
a model using the features of gaze transition, mutual gaze occurrence, gaze
duration, and eye movement distance provides the best performance and can
estimate the user's conversational engagement accurately. The estimation model
is then implemented as a real-time disengagement judgment mechanism and
incorporated into a multimodal dialog manager in an animated conversational
agent. This agent is designed to estimate the user's conversational engagement
and generate probing questions when the user is distracted from the
conversation. Finally, we evaluate the engagement-sensitive agent and find that
asking probing questions at the proper times has the expected effects on the
user's verbal/nonverbal behaviors during communication with the agent. We also
find that our agent system improves the user's impression of the agent in terms
of its engagement awareness, behavior appropriateness, conversation smoothness,
favorability, and intelligence.

Eye gaze is an important means for controlling interaction and coordinating
the participants' turns smoothly. We have studied how eye gaze correlates with
spoken interaction and especially focused on the combined effect of the speech
signal and gazing to predict turn taking possibilities. It is well known that
mutual gaze is important in the coordination of turn taking in two-party
dialogs, and in this article, we investigate whether this fact also holds for
three-party conversations. In group interactions, it may be that different
features are used for managing turn taking than in two-party dialogs. We
collected casual conversational data and used an eye tracker to systematically
observe a participant's gaze in the interactions. By studying the combined
effect of speech and gaze on turn taking, we aimed to answer our main
questions: How well can eye gaze help in predicting turn taking? What is the
role of eye gaze when the speaker holds the turn? Is the role of eye gaze as
important in three-party dialogs as in two-party dialogue? We used Support
Vector Machines (SVMs) to classify turn taking events with respect to speech
and gaze features, so as to estimate how well the features signal a change of
the speaker or a continuation of the same speaker. The results confirm the
earlier hypothesis that eye gaze significantly helps in predicting the
partner's turn taking activity, and we also get supporting evidence for our
hypothesis that the speaker is a prominent coordinator of the interaction
space. Such a turn taking model could be used in interactive applications to
improve the system's conversational performance.

TIIS 2013-10 Volume 3 Issue 3

This recollection of John Riedl, founding coeditor-in-chief of the ACM
Transactions on Interactive Intelligent Systems, presents a picture by editors
of the journal of what it was like to collaborate and interact with him.

Task automation systems promise to increase human productivity by assisting
us with our mundane and difficult tasks. These systems often rely on people to
(1) identify the tasks they want automated and (2) specify the procedural steps
necessary to accomplish those tasks (i.e., to create task models). However, our
interviews with users of a Web task automation system reveal that people find
it difficult to identify tasks to automate and most do not even believe they
perform repetitive tasks worthy of automation. Furthermore, even when
automatable tasks are identified, the well-recognized difficulties of
specifying task steps often prevent people from taking advantage of these
automation systems.
In this research, we analyze real Web usage data and find that people do in
fact repeat behaviors on the Web and that automating these behaviors,
regardless of their complexity, would reduce the overall number of actions
people need to perform when completing their tasks, potentially saving time.
Motivated by these findings, we developed LiveAction, a fully-automated
approach to generating task models from Web usage data. LiveAction models can
be used to populate the task model repositories required by many automation
systems, helping us take advantage of automation in our everyday lives.

Characterizing and Predicting the Multifaceted Nature of Quality in
Educational Web Resources

Efficient learning from Web resources can depend on accurately assessing the
quality of each resource. We present a methodology for developing computational
models of quality that can assist users in assessing Web resources. The
methodology consists of four steps: 1) a meta-analysis of previous studies to
decompose quality into high-level dimensions and low-level indicators, 2) an
expert study to identify the key low-level indicators of quality in the target
domain, 3) human annotation to provide a collection of example resources where
the presence or absence of quality indicators has been tagged, and 4) training
of a machine learning model to predict quality indicators based on content and
link features of Web resources. We find that quality is a multifaceted
construct, with different aspects that may be important to different users at
different times. We show that machine learning models can predict this
multifaceted nature of quality, both in the context of aiding curators as they
evaluate resources submitted to digital libraries, and in the context of aiding
teachers as they develop online educational resources. Finally, we demonstrate
how computational models of quality can be provided as a service, and embedded
into applications such as Web search.

Modern pedagogical software is open-ended and flexible, allowing students to
solve problems through exploration and trial-and-error. Such exploratory
settings provide for a rich educational environment for students, but they
challenge teachers to keep track of students' progress and to assess their
performance. This article presents techniques for recognizing students'
activities in such pedagogical software and visualizing these activities to
teachers. It describes a new plan recognition algorithm that uses a recursive
grammar that takes into account repetition and interleaving of activities. This
algorithm was evaluated empirically using an exploratory environment for
teaching chemistry used by thousands of students in several countries. It was
always able to correctly infer students' plans when the appropriate grammar was
available. We designed two methods for visualizing students' activities for
teachers: one that visualizes students' inferred plans, and one that visualizes
students' interactions over a timeline. Both of these visualization methods
were preferred to and found more helpful than a baseline method which showed a
movie of students' interactions. These results demonstrate the benefit of
combining novel AI techniques and visualization methods for the purpose of
designing collaborative systems that support students in their problem solving
and teachers in their understanding of students' performance.

Recommender systems have already proved to be valuable for coping with the
information overload problem in several application domains. They provide
people with suggestions for items which are likely to be of interest for them;
hence, a primary function of recommender systems is to help people make good
choices and decisions. However, most previous research has focused on
recommendation techniques and algorithms, and less attention has been devoted
to the decision making processes adopted by the users and possibly supported by
the system. There is still a gap between the importance that the community
gives to the assessment of recommendation algorithms and the current range of
ongoing research activities concerning human decision making. Different
decision-psychological phenomena can influence the decision making of users of
recommender systems, and research along these lines is becoming increasingly
important and popular. This special issue highlights how the coupling of
recommendation algorithms with the understanding of human choice and decision
making theory has the potential to benefit research and practice on recommender
systems and to enable users to achieve a good balance between decision accuracy
and decision effort.

An English-Language Argumentation Interface for Explanation Generation with
Markov Decision Processes in the Domain of Academic Advising

A Markov Decision Process (MDP) policy presents, for each state, an action,
which preferably maximizes the expected utility accrual over time. In this
article, we present a novel explanation system for MDP policies. The system
interactively generates conversational English-language explanations of the
actions suggested by an optimal policy, and does so in real time. We rely on
natural language explanations in order to build trust between the user and the
explanation system, leveraging existing research in psychology in order to
generate salient explanations. Our explanation system is designed for
portability between domains and uses a combination of domain-specific and
domain-independent techniques. The system automatically extracts implicit
knowledge from an MDP model and accompanying policy. This MDP-based explanation
system can be ported between applications without additional effort by
knowledge engineers or model builders. Our system separates domain-specific
data from the explanation logic, allowing for a robust system capable of
incremental upgrades. Domain-specific explanations are generated through
case-based explanation techniques specific to the domain and a knowledge base
of concept mappings used to generate English-language explanations.

Personalized systems and recommender systems exploit implicitly and
explicitly provided user information to address the needs and requirements of
those using their services. User preference information, often in the form of
interaction logs and ratings data, is used to identify similar users, whose
opinions are leveraged to inform recommendations or to filter information. In
this work we explore a different dimension of information trends in user bias
and reasoning learned from ratings provided by users to a recommender system.
Our work examines the characteristics of a dataset of 100,000 user ratings on a
corpus of recipes, which illustrates stable user bias towards certain features
of the recipes (cuisine type, key ingredient, and complexity). We exploit this
knowledge to design and evaluate a personalized rating acquisition tool based
on active learning, which leverages user biases in order to obtain ratings
bearing high-value information and to reduce prediction errors with new users.

Making Decisions about Privacy: Information Disclosure in Context-Aware
Recommender Systems

Recommender systems increasingly use contextual and demographical data as a
basis for recommendations. Users, however, often feel uncomfortable providing
such information. In a privacy-minded design of recommenders, users are free to
decide for themselves what data they want to disclose about themselves. But
this decision is often complex and burdensome, because the consequences of
disclosing personal information are uncertain or even unknown. Although a
number of researchers have tried to analyze and facilitate such information
disclosure decisions, their research results are fragmented, and they often do
not hold up well across studies. This article describes a unified approach to
privacy decision research that describes the cognitive processes involved in
users' "privacy calculus" in terms of system-related perceptions and
experiences that act as mediating factors to information disclosure. The
approach is applied in an online experiment with 493 participants using a
mock-up of a context-aware recommender system. Analyzing the results with a
structural linear model, we demonstrate that personal privacy concerns and
disclosure justification messages affect the perception of and experience with
a system, which in turn drive information disclosure decisions. Overall,
disclosure justification messages do not increase disclosure. Although they are
perceived to be valuable, they decrease users' trust and satisfaction. Another
result is that manipulating the order of the requests increases the disclosure
of items requested early but decreases the disclosure of items requested later.

TIIS 2014-01 Volume 3 Issue 4

Integrated online localization and navigation for people with visual
impairments using smart phones

Indoor localization and navigation systems for individuals with Visual
Impairments (VIs) typically rely upon extensive augmentation of the physical
space, significant computational resources, or heavy and expensive sensors;
thus, few systems have been implemented on a large scale. This work describes a
system able to guide people with VIs through indoor environments using
inexpensive sensors, such as accelerometers and compasses, which are available
in portable devices like smart phones. The method takes advantage of feedback
from the human user, who confirms the presence of landmarks, something that
users with VIs already do when navigating in a building. The system calculates
the user's location in real time and uses it to provide audio instructions on
how to reach the desired destination. Initial early experiments suggested that
the accuracy of the localization depends on the type of directions and the
availability of an appropriate transition model for the user. A critical
parameter for the transition model is the user's step length. Consequently,
this work also investigates different schemes for automatically computing the
user's step length and reducing the dependence of the approach on the
definition of an accurate transition model. In this way, the direction
provision method is able to use the localization estimate and adapt to failed
executions of paths by the users. Experiments are presented that evaluate the
accuracy of the overall integrated system, which is executed online on a smart
phone. Both people with VIs and blindfolded sighted people participated in the
experiments, which included paths along multiple floors that required the use
of stairs and elevators.

Fluid gesture interaction design: Applications of continuous recognition for
the design of modern gestural interfaces

This article presents Gesture Interaction DEsigner (GIDE), an innovative
application for gesture recognition. Instead of recognizing gestures only after
they have been entirely completed, as happens in classic gesture recognition
systems, GIDE exploits the full potential of gestural interaction by tracking
gestures continuously and synchronously, allowing users to both control the
target application moment to moment and also receive immediate and synchronous
feedback about system recognition states. By this means, they quickly learn how
to interact with the system in order to develop better performances.
Furthermore, rather than learning the predefined gestures of others, GIDE
allows users to design their own gestures, making interaction more natural and
also allowing the applications to be tailored by users' specific needs. We
describe our system that demonstrates these new qualities -- that combine to
provide fluid gesture interaction design -- through evaluations with a range of
performers and artists.

Design and evaluation techniques for authoring interactive and stylistic
behaviors

We present a series of projects for end-user authoring of interactive
robotic behaviors, with a particular focus on the style of those behaviors: we
call this approach Style-by-Demonstration (SBD). We provide an overview
introduction of three different SBD platforms: SBD for animated character
interactive locomotion paths, SBD for interactive robot locomotion paths, and
SBD for interactive robot dance. The primary contribution of this article is a
detailed cross-project SBD analysis of the interaction designs and evaluation
approaches employed, with the goal of providing general guidelines stemming
from our experiences, for both developing and evaluating SBD systems. In
addition, we provide the first full account of our Puppet Master SBD algorithm,
with an explanation of how it evolved through the projects.

Conversational agent technology is an emerging paradigm for creating a
social environment in online groups that is conducive to effective teamwork.
Prior work has demonstrated advantages in terms of learning gains and
satisfaction scores when groups learning together online have been supported by
conversational agents that employ Balesian social strategies. This prior work
raises two important questions that are addressed in this article. The first
question is one of generality. Specifically, are the positive effects of the
designed support specific to learning contexts? Or are they in evidence in
other collaborative task domains as well? We present a study conducted within a
collaborative decision-making task where we see that the positive effects of
the Balesian social strategies extend to this new context. The second question
is whether it is possible to increase the effectiveness of the Balesian social
strategies by increasing the context sensitivity with which the social
strategies are triggered. To this end, we present technical work that increases
the sensitivity of the triggering. Next, we present a user study that
demonstrates an improvement in performance of the support agent with the new,
more sensitive triggering policy over the baseline approach from prior work.
The technical contribution of this article is that we extend prior work
where such support agents were modeled using a composition of conversational
behaviors integrated within an event-driven framework. Within the present
approach, conversation is orchestrated through context-sensitive triggering of
the composed behaviors. The core effort involved in applying this approach
involves building a set of triggering policies that achieve this orchestration
in a time-sensitive and coherent manner. In line with recent developments in
data-driven approaches for building dialog systems, we present a novel
technique for learning behavior-specific triggering policies, deploying it as
part of our efforts to improve a socially capable conversational tutor agent
that supports collaborative learning.

The Service-Oriented Computing (SOC) paradigm is currently being adopted by
many developers, as it promises the construction of applications through reuse
of existing Web Services (WSs). However, current SOC tools produce applications
that interact with users in a limited way. This limitation is overcome by
model-based Human-Computer Interaction (HCI) approaches that support the
development of applications whose functionality is realized with WSs and whose
User Interface (UI) is adapted to the user's context. Typically, such
approaches do not consider various functional issues, such as the applications'
semantics and their syntactic robustness in terms of the WSs selected to
implement their functionality and the automation of the service discovery and
selection processes. To this end, we propose a model-driven design method for
interactive service-based applications that is able to consider the functional
issues and their implications for the UI. This method is realized by a
semiautomatic environment that can be integrated into current model-based HCI
tools to complete the development of interactive service front-ends. The
proposed method takes as input an HCI task model, which includes the user's
view of the interactive system, and produces a concrete service model that
describes how existing services can be combined to realize the application's
functionality. To achieve its goal, our method first transforms system tasks
into semantic service queries by mapping the task objects onto domain ontology
concepts; then it sends each resulting query to a semantic service engine so as
to discover the corresponding services. In the end, only one service from those
associated with a system task is selected, through the execution of a novel
service concretization algorithm that ensures message compatibility between the
selected services.

Content-based tag propagation and tensor factorization for personalized item
recommendation based on social tagging

In this article, a novel method for personalized item recommendation based
on social tagging is presented. The proposed approach comprises a content-based
tag propagation method to address the sparsity and "cold start" problems, which
often occur in social tagging systems and decrease the quality of
recommendations. The proposed method exploits (a) the content of items and (b)
users' tag assignments through a relevance feedback mechanism in order to
automatically identify the optimal number of content-based and conceptually
similar items. The relevance degrees between users, tags, and conceptually
similar items are calculated in order to ensure accurate tag propagation and
consequently to address the issue of "learning tag relevance." Moreover, the
ternary relation among users, tags, and items is preserved by performing tag
propagation in the form of triplets based on users' personal preferences and
"cold start" degree. The latent associations among users, tags, and items are
revealed based on a tensor factorization model in order to build personalized
item recommendations. In our experiments with real-world social data, we show
the superiority of the proposed approach over other state-of-the-art methods,
since several problems in social tagging systems are successfully tackled.
Finally, we present the recommendation methodology in the multimodal engine of
I-SEARCH, where users' interaction capabilities are demonstrated.